Division of Cell Matrix Biology & Regenerative Medicine, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, Manchester M13 9PT, UK.
Monash Bioinformatics Platform, Monash University, Melbourne, VIC 3800, Australia.
Int J Mol Sci. 2021 Mar 17;22(6):3071. doi: 10.3390/ijms22063071.
Both protease- and reactive oxygen species (ROS)-mediated proteolysis are thought to be key effectors of tissue remodeling. We have previously shown that comparison of amino acid composition can predict the differential susceptibilities of proteins to photo-oxidation. However, predicting protein susceptibility to endogenous proteases remains challenging. Here, we aim to develop bioinformatics tools to (i) predict cleavage site locations (and hence putative protein susceptibilities) and (ii) compare the predicted vulnerabilities of skin proteins to protease- and ROS-mediated proteolysis. The first goal of this study was to experimentally evaluate the ability of existing protease cleavage site prediction models (PROSPER and DeepCleave) to identify experimentally determined MMP9 cleavage sites in two purified proteins and in a complex human dermal fibroblast-derived extracellular matrix (ECM) proteome. We subsequently developed deep bidirectional recurrent neural network (BRNN) models to predict cleavage sites for 14 tissue proteases. The predictions of the new models were tested against experimental datasets and combined with amino acid composition analysis (to predict ultraviolet radiation (UVR)/ROS susceptibility) in a new web app: the Manchester proteome susceptibility calculator (MPSC). The BRNN models performed better in predicting cleavage sites in native dermal ECM proteins than existing models (DeepCleave and PROSPER), and application of MPSC to the skin proteome suggests that: compared with the elastic fiber network, fibrillar collagens may be susceptible primarily to protease-mediated proteolysis. We also identify additional putative targets of oxidative damage (dermatopontin, fibulins and defensins) and protease action (laminins and nidogen). MPSC has the potential to identify potential targets of proteolysis in disparate tissues and disease states.
蛋白水解和活性氧物种（ROS）介导的蛋白水解都被认为是组织重塑的关键效应物。我们之前已经表明，比较氨基酸组成可以预测蛋白质对光氧化的差异敏感性。然而，预测蛋白质对内源性蛋白酶的敏感性仍然具有挑战性。在这里，我们旨在开发生物信息学工具来：（i）预测切割位点位置（从而推测蛋白质的敏感性）和（ii）比较皮肤蛋白对蛋白酶和 ROS 介导的蛋白水解的预测易感性。本研究的第一个目标是实验评估现有蛋白酶切割位点预测模型（PROSPER 和 DeepCleave）识别两种纯化蛋白和复杂人真皮成纤维细胞衍生细胞外基质（ECM）蛋白质组中实验确定的 MMP9 切割位点的能力。随后，我们开发了深度双向递归神经网络（BRNN）模型来预测 14 种组织蛋白酶的切割位点。新模型的预测结果通过实验数据集进行了测试，并与氨基酸组成分析（预测紫外线（UVR）/ROS 敏感性）相结合，在一个新的网络应用程序中：曼彻斯特蛋白质组易感性计算器（MPSC）。BRNN 模型在预测天然真皮 ECM 蛋白中的切割位点方面表现优于现有模型（DeepCleave 和 PROSPER），并且将 MPSC 应用于皮肤蛋白质组表明，与弹性纤维网络相比，纤维胶原可能主要容易受到蛋白酶介导的蛋白水解。我们还确定了氧化损伤（真皮蛋白聚糖、纤维连接蛋白和防御素）和蛋白酶作用（层粘连蛋白和巢蛋白）的其他潜在靶标。MPSC 有可能识别不同组织和疾病状态中潜在的蛋白水解靶标。
